TY - GEN
T1 - Two-Level Hierarchical Mission-Based Model Predictive Control
AU - Koeln, Justin P.
AU - Alleyne, Andrew G.
N1 - Funding Information:
*Research supported by the National Science Foundation Engineering Research Center for Power Optimization of Electro Thermal Systems (POETS) with cooperative agreement EEC-1449548.
Publisher Copyright:
© 2018 AACC.
PY - 2018/8/9
Y1 - 2018/8/9
N2 - A two-level hierarchical model predictive control (MPC) formulation is presented for constrained linear systems operating over a mission. Mission-based MPC is applicable to many control applications where the system operates for a finite time and stability about an equilibrium is not the primary objective. Instead, the primary control objective is to guarantee constraint satisfaction during operation as well as terminal constraints imposed on the final state of the system at the end of the mission. The secondary control objective is reference tracking, where references determine the desired operation for the system. A hierarchical control formulation permits the upper level controller to plan state trajectories over the entire mission, while a lower level controller modifies these trajectories to improve reference tracking. This decomposition of the control problem reduces computational cost, enabling real-time implementation for large systems with long missions. Feasibility proofs guarantee the constraint satisfaction while a numerical example demonstrates the efficacy of the approach.
AB - A two-level hierarchical model predictive control (MPC) formulation is presented for constrained linear systems operating over a mission. Mission-based MPC is applicable to many control applications where the system operates for a finite time and stability about an equilibrium is not the primary objective. Instead, the primary control objective is to guarantee constraint satisfaction during operation as well as terminal constraints imposed on the final state of the system at the end of the mission. The secondary control objective is reference tracking, where references determine the desired operation for the system. A hierarchical control formulation permits the upper level controller to plan state trajectories over the entire mission, while a lower level controller modifies these trajectories to improve reference tracking. This decomposition of the control problem reduces computational cost, enabling real-time implementation for large systems with long missions. Feasibility proofs guarantee the constraint satisfaction while a numerical example demonstrates the efficacy of the approach.
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U2 - 10.23919/ACC.2018.8431370
DO - 10.23919/ACC.2018.8431370
M3 - Conference contribution
AN - SCOPUS:85052597537
SN - 9781538654286
T3 - Proceedings of the American Control Conference
SP - 2332
EP - 2337
BT - 2018 Annual American Control Conference, ACC 2018
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2018 Annual American Control Conference, ACC 2018
Y2 - 27 June 2018 through 29 June 2018
ER -